• DocumentCode
    724290
  • Title

    A study on autonomous learning mechanism of Cognitive robot

  • Author

    Shi Tao ; Ren Hongge ; Yin Rui ; Xiang Yingfan

  • Author_Institution
    Coll. of Electr. Eng., Hebei United Univ., Tangshan, China
  • fYear
    2015
  • fDate
    23-25 May 2015
  • Firstpage
    3243
  • Lastpage
    3247
  • Abstract
    Aiming at the movement balance control problem of the robot, this paper presents a sensorimotor system Cognitive model based on operant conditioning principle, and researches the working cooperation among its interior nerve organs, so that a sensorimotor system is established. The Cognitive model can realize the sensorimotor mapping from states to actions by supervised learning, and carry out the probabilistic choice based on operant conditioning principle to actions using the action forecast evaluation results, thereby, the robot obtains the self-learning ability like human or animal through interacting, studying and training with the unknown environment, and realized the movement balance control to the robot. Consequently, the paper makes some simulation experiments on the robot, and the results indicate that this model has the better Cognitive characters and make the robot master the movement balance control skill through autonomic learning.
  • Keywords
    cognitive systems; learning (artificial intelligence); mobile robots; motion control; probability; action forecast evaluation; autonomic learning; autonomous learning mechanism; cognitive characters; cognitive robot model; interior nerve organs; movement balance control problem; operant conditioning principle; probabilistic choice; self-learning ability; sensorimotor mapping system; supervised learning; Animals; Basal ganglia; Biological system modeling; Brain modeling; Mobile robots; Robot sensing systems; Cognitive Model; Movement Balance Control; Operant Conditioning; Robot; Sensorimotor System;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (CCDC), 2015 27th Chinese
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4799-7016-2
  • Type

    conf

  • DOI
    10.1109/CCDC.2015.7162479
  • Filename
    7162479